Many workflows in the healthcare industry (and other industries) require information to be extracted from a variety of data sources and for the extracted information to be used within the workflow according to the particular nature of the workflow. As one example, consider a patient who has undergone surgery at a hospital. After the surgery has been completed, the hospital must generate a bill for all of the services provided and materials used by the hospital in connection with the surgery, including pre- and post-surgical care. Generating such a bill may require obtaining information from a variety of data sources, such as transcripts of notes dictated by the surgeon (possibly before, during, and after the surgery); typewritten notes typed by the surgeon, nurses, anesthesiologist, and other medical personnel; and data stored within an Electronic Health Record (EHR) that stores a variety of information about the patient. For example, if the bill is to be sent to the patient it may be necessary to extract from such data sources information about the kind of surgery performed; the number of doctors; nurses, and other personnel who participated in the surgery; the equipment that was used in the surgery; the materials that were used to perform the surgery; the amount of time the patient stayed in the hospital after the surgery; and the medications that were prescribed to the patient as a result of the surgery. If the bill is to be sent to an insurer, then the hospital may need to provide information specified by a standard mapping referred to as a Diagnostic Related Grouping (DRG). DRGs represent standardized payment structures, such as a specific amount to be paid to the hospital for a particular type of surgery.
Extracting such a wide variety of information from such a wide variety and large number of sources can be difficult, time-consuming, and prone to error.